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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">src/caffe/layers</a> - embed_layer.cpp<span style="font-size: 80%;"> (source / <a href="embed_layer.cpp.func-sort-c.html">functions</a>)</span></td>
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            <td width="10%" class="headerCovTableHead">Hit</td>
            <td width="10%" class="headerCovTableHead">Total</td>
            <td width="15%" class="headerCovTableHead">Coverage</td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">62</td>
            <td class="headerCovTableEntryLo">3.2 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:50:33</td>
            <td></td>
            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">2</td>
            <td class="headerCovTableEntry">16</td>
            <td class="headerCovTableEntryLo">12.5 %</td>
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            <td class="headerItem">Legend:</td>
            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #include &lt;vector&gt;</a>
<span class="lineNum">       2 </span>            : 
<span class="lineNum">       3 </span>            : #include &quot;caffe/filler.hpp&quot;
<span class="lineNum">       4 </span>            : #include &quot;caffe/layers/embed_layer.hpp&quot;
<span class="lineNum">       5 </span>            : #include &quot;caffe/util/math_functions.hpp&quot;
<span class="lineNum">       6 </span>            : 
<span class="lineNum">       7 </span>            : namespace caffe {
<span class="lineNum">       8 </span>            : 
<span class="lineNum">       9 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      10 </span><span class="lineNoCov">          0 : void EmbedLayer&lt;Dtype&gt;::LayerSetUp(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      11 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      12 </span><span class="lineNoCov">          0 :   N_ = this-&gt;layer_param_.embed_param().num_output();</span>
<span class="lineNum">      13 </span><span class="lineNoCov">          0 :   CHECK_GT(N_, 0) &lt;&lt; &quot;EmbedLayer num_output must be positive.&quot;;</span>
<span class="lineNum">      14 </span><span class="lineNoCov">          0 :   K_ = this-&gt;layer_param_.embed_param().input_dim();</span>
<span class="lineNum">      15 </span><span class="lineNoCov">          0 :   CHECK_GT(K_, 0) &lt;&lt; &quot;EmbedLayer input_dim must be positive.&quot;;</span>
<span class="lineNum">      16 </span><span class="lineNoCov">          0 :   bias_term_ = this-&gt;layer_param_.embed_param().bias_term();</span>
<span class="lineNum">      17 </span>            :   // Check if we need to set up the weights
<span class="lineNum">      18 </span><span class="lineNoCov">          0 :   if (this-&gt;blobs_.size() &gt; 0) {</span>
<span class="lineNum">      19 </span><span class="lineNoCov">          0 :     LOG(INFO) &lt;&lt; &quot;Skipping parameter initialization&quot;;</span>
<span class="lineNum">      20 </span>            :   } else {
<span class="lineNum">      21 </span><span class="lineNoCov">          0 :     if (bias_term_) {</span>
<span class="lineNum">      22 </span><span class="lineNoCov">          0 :       this-&gt;blobs_.resize(2);</span>
<span class="lineNum">      23 </span>            :     } else {
<span class="lineNum">      24 </span><span class="lineNoCov">          0 :       this-&gt;blobs_.resize(1);</span>
<span class="lineNum">      25 </span>            :     }
<span class="lineNum">      26 </span>            :     // Initialize the weights --
<span class="lineNum">      27 </span>            :     // transposed from InnerProductLayer for spatial locality.
<span class="lineNum">      28 </span><span class="lineNoCov">          0 :     vector&lt;int&gt; weight_shape(2);</span>
<span class="lineNum">      29 </span><span class="lineNoCov">          0 :     weight_shape[0] = K_;</span>
<span class="lineNum">      30 </span><span class="lineNoCov">          0 :     weight_shape[1] = N_;</span>
<span class="lineNum">      31 </span><span class="lineNoCov">          0 :     this-&gt;blobs_[0].reset(new Blob&lt;Dtype&gt;(weight_shape));</span>
<span class="lineNum">      32 </span>            :     // fill the weights
<span class="lineNum">      33 </span>            :     shared_ptr&lt;Filler&lt;Dtype&gt; &gt; weight_filler(GetFiller&lt;Dtype&gt;(
<span class="lineNum">      34 </span><span class="lineNoCov">          0 :         this-&gt;layer_param_.embed_param().weight_filler()));</span>
<span class="lineNum">      35 </span><span class="lineNoCov">          0 :     weight_filler-&gt;Fill(this-&gt;blobs_[0].get());</span>
<span class="lineNum">      36 </span>            :     // If necessary, initialize and fill the bias term
<span class="lineNum">      37 </span><span class="lineNoCov">          0 :     if (bias_term_) {</span>
<span class="lineNum">      38 </span><span class="lineNoCov">          0 :       vector&lt;int&gt; bias_shape(1, N_);</span>
<span class="lineNum">      39 </span><span class="lineNoCov">          0 :       this-&gt;blobs_[1].reset(new Blob&lt;Dtype&gt;(bias_shape));</span>
<span class="lineNum">      40 </span>            :       shared_ptr&lt;Filler&lt;Dtype&gt; &gt; bias_filler(GetFiller&lt;Dtype&gt;(
<span class="lineNum">      41 </span><span class="lineNoCov">          0 :           this-&gt;layer_param_.embed_param().bias_filler()));</span>
<span class="lineNum">      42 </span><span class="lineNoCov">          0 :       bias_filler-&gt;Fill(this-&gt;blobs_[1].get());</span>
<span class="lineNum">      43 </span>            :     }
<span class="lineNum">      44 </span>            :   }  // parameter initialization
<span class="lineNum">      45 </span><span class="lineNoCov">          0 :   this-&gt;param_propagate_down_.resize(this-&gt;blobs_.size(), true);</span>
<span class="lineNum">      46 </span><span class="lineNoCov">          0 : }</span>
<span class="lineNum">      47 </span>            : 
<span class="lineNum">      48 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      49 </span><span class="lineNoCov">          0 : void EmbedLayer&lt;Dtype&gt;::Reshape(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      50 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      51 </span>            :   // Figure out the dimensions
<span class="lineNum">      52 </span><span class="lineNoCov">          0 :   M_ = bottom[0]-&gt;count();</span>
<span class="lineNum">      53 </span><span class="lineNoCov">          0 :   vector&lt;int&gt; top_shape = bottom[0]-&gt;shape();</span>
<span class="lineNum">      54 </span><span class="lineNoCov">          0 :   top_shape.push_back(N_);</span>
<span class="lineNum">      55 </span><span class="lineNoCov">          0 :   top[0]-&gt;Reshape(top_shape);</span>
<span class="lineNum">      56 </span>            :   // Set up the bias multiplier
<span class="lineNum">      57 </span><span class="lineNoCov">          0 :   if (bias_term_) {</span>
<span class="lineNum">      58 </span><span class="lineNoCov">          0 :     vector&lt;int&gt; bias_shape(1, M_);</span>
<span class="lineNum">      59 </span><span class="lineNoCov">          0 :     bias_multiplier_.Reshape(bias_shape);</span>
<span class="lineNum">      60 </span><span class="lineNoCov">          0 :     caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());</span>
<span class="lineNum">      61 </span>            :   }
<span class="lineNum">      62 </span><span class="lineNoCov">          0 : }</span>
<a name="63"><span class="lineNum">      63 </span>            : </a>
<span class="lineNum">      64 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      65 </span><span class="lineNoCov">          0 : void EmbedLayer&lt;Dtype&gt;::Forward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      66 </span>            :     const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      67 </span><span class="lineNoCov">          0 :   const Dtype* bottom_data = bottom[0]-&gt;cpu_data();</span>
<span class="lineNum">      68 </span><span class="lineNoCov">          0 :   const Dtype* weight = this-&gt;blobs_[0]-&gt;cpu_data();</span>
<span class="lineNum">      69 </span><span class="lineNoCov">          0 :   Dtype* top_data = top[0]-&gt;mutable_cpu_data();</span>
<span class="lineNum">      70 </span>            :   int index;
<span class="lineNum">      71 </span><span class="lineNoCov">          0 :   for (int n = 0; n &lt; M_; ++n) {</span>
<span class="lineNum">      72 </span><span class="lineNoCov">          0 :     index = static_cast&lt;int&gt;(bottom_data[n]);</span>
<span class="lineNum">      73 </span>            :     DCHECK_GE(index, 0);
<span class="lineNum">      74 </span>            :     DCHECK_LT(index, K_);
<span class="lineNum">      75 </span>            :     DCHECK_EQ(static_cast&lt;Dtype&gt;(index), bottom_data[n]) &lt;&lt; &quot;non-integer input&quot;;
<span class="lineNum">      76 </span><span class="lineNoCov">          0 :     caffe_copy(N_, weight + index * N_, top_data + n * N_);</span>
<span class="lineNum">      77 </span>            :   }
<span class="lineNum">      78 </span><span class="lineNoCov">          0 :   if (bias_term_) {</span>
<span class="lineNum">      79 </span><span class="lineNoCov">          0 :     const Dtype* bias = this-&gt;blobs_[1]-&gt;cpu_data();</span>
<span class="lineNum">      80 </span><span class="lineNoCov">          0 :     caffe_cpu_gemm&lt;Dtype&gt;(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),</span>
<span class="lineNum">      81 </span>            :         bias_multiplier_.cpu_data(), bias, Dtype(1), top_data);
<span class="lineNum">      82 </span>            :   }
<span class="lineNum">      83 </span><span class="lineNoCov">          0 : }</span>
<a name="84"><span class="lineNum">      84 </span>            : </a>
<span class="lineNum">      85 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      86 </span><span class="lineNoCov">          0 : void EmbedLayer&lt;Dtype&gt;::Backward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,</span>
<span class="lineNum">      87 </span>            :     const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom) {
<span class="lineNum">      88 </span><span class="lineNoCov">          0 :   CHECK(!propagate_down[0]) &lt;&lt; &quot;Can't backpropagate to EmbedLayer input.&quot;;</span>
<span class="lineNum">      89 </span><span class="lineNoCov">          0 :   if (this-&gt;param_propagate_down_[0]) {</span>
<span class="lineNum">      90 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">      91 </span><span class="lineNoCov">          0 :     const Dtype* bottom_data = bottom[0]-&gt;cpu_data();</span>
<span class="lineNum">      92 </span>            :     // Gradient with respect to weight
<span class="lineNum">      93 </span><span class="lineNoCov">          0 :     Dtype* weight_diff = this-&gt;blobs_[0]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">      94 </span>            :     int index;
<span class="lineNum">      95 </span><span class="lineNoCov">          0 :     for (int n = 0; n &lt; M_; ++n) {</span>
<span class="lineNum">      96 </span><span class="lineNoCov">          0 :       index = static_cast&lt;int&gt;(bottom_data[n]);</span>
<span class="lineNum">      97 </span>            :       DCHECK_GE(index, 0);
<span class="lineNum">      98 </span>            :       DCHECK_LT(index, K_);
<span class="lineNum">      99 </span>            :       DCHECK_EQ(static_cast&lt;Dtype&gt;(index), bottom_data[n])
<span class="lineNum">     100 </span>            :           &lt;&lt; &quot;non-integer input&quot;;
<span class="lineNum">     101 </span><span class="lineNoCov">          0 :       caffe_axpy(N_, Dtype(1), top_diff + n * N_, weight_diff + index * N_);</span>
<span class="lineNum">     102 </span>            :     }
<span class="lineNum">     103 </span>            :   }
<span class="lineNum">     104 </span><span class="lineNoCov">          0 :   if (bias_term_ &amp;&amp; this-&gt;param_propagate_down_[1]) {</span>
<span class="lineNum">     105 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[0]-&gt;cpu_diff();</span>
<span class="lineNum">     106 </span><span class="lineNoCov">          0 :     Dtype* bias_diff = this-&gt;blobs_[1]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">     107 </span><span class="lineNoCov">          0 :     caffe_cpu_gemv&lt;Dtype&gt;(CblasTrans, M_, N_, Dtype(1), top_diff,</span>
<span class="lineNum">     108 </span>            :         bias_multiplier_.cpu_data(), Dtype(1), bias_diff);
<span class="lineNum">     109 </span>            :   }
<span class="lineNum">     110 </span><span class="lineNoCov">          0 : }</span>
<a name="111"><span class="lineNum">     111 </span>            : </a>
<span class="lineNum">     112 </span>            : #ifdef CPU_ONLY
<span class="lineNum">     113 </span><span class="lineNoCov">          0 : STUB_GPU(EmbedLayer);</span>
<span class="lineNum">     114 </span>            : #endif
<span class="lineNum">     115 </span>            : 
<span class="lineNum">     116 </span>            : INSTANTIATE_CLASS(EmbedLayer);
<a name="117"><span class="lineNum">     117 </span><span class="lineCov">          3 : REGISTER_LAYER_CLASS(Embed);</span></a>
<span class="lineNum">     118 </span>            : 
<span class="lineNum">     119 </span><span class="lineCov">          3 : }  // namespace caffe</span>
</pre>
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